EARNINGS MANAGEMENT PROXIES: PRUDENT BUSINESS DECISIONS OR EARNINGS MANIPULATION?*

Theodore E. Christensen

The University of Georgia

Adrienna Huffman** Tulane University

Melissa Lewis-Western The University of Utah

Brigham Young University

February 2016

                                                            * We thank Won Kim, Tim Seidel, Steve Stubben, Kristen Valentine, and participants at the 2015 BYU Accounting Research Symposium for their helpful comments. We thank Karen Hennes, Andy Leone, and Brian Miller for use of use of their restatement data. ** Email: adrienna.huffman@tulane.edu; Phone: 504.314.7451; Address: A.B Freeman School of Business, Tulane University, 7 McAlister Dr., New Orleans, LA.

EARNINGS MANAGEMENT PROXIES:

PRUDENT BUSINESS DECISIONS OR EARNINGS MANIPULATION?

ABSTRACT Empirical measures of earnings manipulation should be negatively associated with future operating performance when accruals reverse and the implications of real-activities-based earnings manipulation are realized. But are they? We investigate several common empirical proxies for earnings management and explore whether they capture the underlying construct of earnings manipulation by investigating their association with future performance and analysts’ forecast errors. We find that two common measures of “earnings manipulation” are actually associated with improvements in firms’ future operating performance and lower subsequent analysts’ forecast errors, indicating that these measures do not appear to capture the characteristics of earnings manipulation, but rather reflect prudent business decisions or income smoothing. We validate the results by examining the correspondence between the earnings management proxies and restatements.

Keywords: earnings management; financial reporting quality

Data Availability: Data is publicly available from the sources identified in the text. JEL: M41, M41,

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1. INTRODUCTION

We examine whether several commonly-used earnings management measures exhibit the

characteristics we would expect of strategic earnings manipulation. Strategic earnings

manipulation refers to managers’ use of judgments in financial reporting and in structuring

transactions to: (1) mislead stakeholders about the firm’s underlying performance, or (2) influence

contractual outcomes (Schipper 1989; Healy and Wahlen 1999).1 Thus, strategic earnings

manipulation should be negatively associated with future operating performance when accruals

reverse and the implications of real-activities-based earnings manipulation are realized (e.g., Stein

1989; Fan 2007; Bowen et al. 2008; Dechow et al. 2012). Moreover, to the extent that managers

manipulate reported earnings, stakeholders who rely on financial statements, such as financial

analysts, are likely to forecast future earnings with error (e.g., Bradshaw et al. 2001; Abarbanell

and Lehavy 2003; Burgstahler and Eames 2003). In these cases, strategic earnings manipulation

proxies should also be associated with analysts’ forecast errors in future periods when subsequent

performance reveals the earnings manipulation.

Much of the prior research examining earnings manipulation assumes that increases in

earnings management measures capture the underlying construct of earnings manipulation, but this

conclusion is premature if the increase is attributable to growth, performance, or strategic business

decisions.2 As a result, prior research has used simulations, additional controls for performance,

                                                            1 In contrast, we use the term earnings management to refer to measures used in extant financial reporting research (e.g., cuts in discretionary expenses) or to refer to actions undertaken by managers to smooth earnings or to signal information. 2 Although most earnings management papers do not examine the relation between earnings manipulation measures and performance, there are a few notable exceptions including: Bowen et al. 2008, Guay et al. 1996, and Subramanyan 1996; Cohen and Zarowin 2010; Kothari, Mizik, and Roychowdhury 2015. Our paper is distinct from this prior research in that we consider accrual, real, and special items based earnings manipulation concurrently, we examine multiple measures of future performance including analysts’ forecast errors and restatements, and we conduct a within-firm analysis. In addition, we consider a broad sample of firms rather than subsets of firms (e.g., SEO firms). Thus, the contribution of our paper is to provide the literature with a broad analysis of earnings manipulation measures that can aid researchers in their choice of measures.

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and the correspondence between the measures and indicators of fraud to assess the construct

validity of the earnings management measures (Dechow et al. 1995; Kothari et al. 2005; Jones et

al. 2008; Stubben 2010; Cohen et al. 2015). Although these studies provide important insight on

construct validity, they do not provide a comprehensive analysis of the relation between earnings

management measures and subsequent performance, which is an important fundamental

characteristic that should be considered when conducting and consuming earnings manipulation

research.

In summary, earnings management measures have not been well vetted regarding the extent

to which they associate negatively with future performance, and, thus, possess a primary

characteristic of earnings manipulation. If empirical earnings management proxies actually reflect

the underlying construct of earnings manipulation, we would expect them to be associated with:

(1) declining future operating performance (e.g., Bowen et al. 2008), and (2) an increase in the

magnitude of analysts’ forecast errors (e.g., Bradshaw et al. 2001).3 If empirical proxies do not

exhibit these properties, then it is unlikely that they actually capture strategic earnings

manipulation.

While some recent research develops new methods to better identify earnings manipulation

(Dechow et al. 2012; Ecker et al. 2013; Gerakos and Kovrijnykh 2013; Cohen et al. 2015), we note

two important problems: (1) these methods are not practical in all situations and (2) traditional

earnings management measures are still pervasive in the literature.4 Thus, we approach the

problem of choosing a suitable measure of earnings manipulation from an alternative perspective

                                                            3 However, if analysts are able to unwind the earnings manipulation, then earnings manipulation will not be associated with future forecast errors. 4 This is an important question as some of the new measures (e.g., Dechow et al. 2012) cannot be implemented in all situations. Further, an examination of the top five accounting journals over the past three years (2012-2014) indicates that 31 out of 71 (44%) financial archival earnings management studies use at least one of the six earnings management metrics we consider in this paper. See AppendixA.

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that complements prior research. We focus on the extent to which commonly-used earnings

management measures exhibit the characteristics we would expect of earnings manipulation,

namely poor operating performance and increased forecast errors in subsequent periods when

accruals reverse and the implications of real earnings management are realized.

We study the relation between earnings management and subsequent performance from

1990-2011 and we consider six commonly used empirical earnings management measures. First,

we examine two accruals-based metrics: abnormal accruals calculated using the cross-sectional

Jones (1991) model (e.g., DeFond and Subramanyam, 1998); and abnormal revenue (Stubben

2006, 2010).5 Next, we measure real-activities-based earnings management using abnormal cuts

to discretionary expenses, unusually low cash flow from operations, and excess production (Cohen

and Zarowin, 2010; Gunny, 2010; Roychowdhury, 2006). Finally, we measure classification

shifting as the misclassification of recurring expenses as special items following Cain et al. (2013).

Contrary to our expectations, we find that quarters with greater abnormal accruals from

the cross-sectional version of the Jones (1991) model (hereafter Jones-type models) and quarters

with abnormally low discretionary expenses are associated with stronger future operating

performance, and smaller analysts’ forecast errors, relative to the firm’s average in other quarters.6

Thus, the results suggest that these measures either do not reflect true strategic earnings

manipulation, or the implications of these earnings manipulation tools occur more than 24 months

later (and are thus outside the timeframe over which we measure future performance).7 In contrast,

                                                            5 We do not examine the Dechow and Dichev (2002) accruals quality metric because it is an annual metric that requires several years of data to estimate, and our design requires quarterly measures of earnings manipulation. 6 We find similar results if we use unsigned Jones-type abnormal accruals and when we calculate Jones-type accruals with the Dechow, Sloan, and Sweeny (1995) modification, but the modification is imperfect in our setting as we don’t have an estimation period that is distinct from the treatment period. Hence, our decision not to use modified Jones abnormal accruals as our main measure of Jones-type accruals. 7 Our main analyses examine future performance over the 12 months following the measurement of earnings management. In robustness analyses discussed in section 4.3, we also examine future performance over 24 months.

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we find that abnormal revenues, excess production, lower-than-expected cash flows and

classification shifting are associated with lower future performance and larger analysts’ forecast

errors. Thus, these measures possess characteristics that we would expect of earnings

manipulation.

Our final set of analyses explores whether the earnings manipulation proxies we consider

increase in settings where prior research finds that managers engage in earnings manipulation (see

Dechow et al. 2011; Dechow et al. 2012). Specifically, we examine earnings restatements. We find

that, consistent with our previous tests, abnormal revenue, excess production, lower-than-expected

cash flows and classification shifting are significantly positively associated with earnings

restatements. The association between real earnings management and restatements may seem

counterintuitive, but Badertscher (2011) provides evidence that real earnings management is used

after accrual earnings management and thus corresponds with more aggressive earnings

management strategies that cumulate with material misstatements or even fraudulent reporting

(i.e., resulting in restatements). In contrast, and consistent with our prior tests, we find that Jones-

type abnormal accruals are significantly negatively associated with earnings restatements, and

abnormally low discretionary expenses are not significantly associated with earnings

restatements.8 This evidence provides further evidence consistent with our previous results, that

abnormal revenue, excess production, lower-than-expected cash flows and classification shifting

capture earnings manipulation, while Jones-type abnormal accruals and abnormally low

discretionary expenses do not.

                                                            8 This evidence is consistent with prior work that documents a low detection rate for Jones-type model accruals when examining fraud (Beneish 1997; Dechow et al. 1995). Further, Jones et al. (2008) conclude that Jones-type abnormal accruals are not associated with restatements.

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We note that a pervasive concern with earnings manipulation studies is misspecification

due to correlated omitted variables (e.g., McNichols 2002; Dechow et al. 2012;). Moreover, many

of the potential omitted factors persist over time (Dechow et al. 2012; Ecker et al. 2013). In order

to address these concerns, we employ firm fixed effects in our analyses. Firm fixed effects control

for persistent firm characteristics and thus reduce the effects of correlated omitted variables

without requiring specific identification of those variables (e.g., Wooldridge 2000). In addition, to

control for time-varying factors, we include control variables for innate earnings quality, growth,

and performance. These design choices help to reduce common concerns associated with studying

earnings management and help to increase confidence in our results.

This study makes several contributions to the earnings management literature. First, an

extensive body of research attempts to measure earnings management and documents situations

when it is expected to occur (see Dechow et al. (2010) for a review). We contribute to this literature

by providing a systematic investigation into the association between three types of earnings

manipulation measures (abnormal accruals, real earnings management, and classification shifting)

and two important future outcomes (firms’ future operating performance and analysts’ forecast

errors). These analyses provide evidence on the extent to which commonly used earnings

management proxies exhibit characteristics we would expect of strategic earnings manipulation

and complement research that assesses the validity of the earnings management measures with

simulations by examining their correlation with fraud indicators, and by better identifying normal

accruals and real operating activities (e.g., Dechow et al. 1995; Kothari et al. 2005; Jones et al.

2008; Stubben 2010; Cohen et al. 2015). An important implication of our study is that some

evidence from prior research documenting an increase in abnormal accruals (using Jones-type

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models) and abnormal cuts in discretionary expenses, may not actually indicate strategic earnings

manipulation, but instead prudent business decisions or income smoothing.

Further, our results echo those of earlier research documenting a positive relation between

measures of Jones-type abnormal accruals and future performance (e.g., Bowen et al. 2008; Guay

et al. 1996; Subramanyan 1996). We extend this literature by (1) considering both accrual and real

earnings management concurrently, (2) examining multiple measures of future performance, and

(3) examining variation in earnings manipulation within a firm (rather than in the cross-section).

Our results suggest that two widely used measures of earnings manipulation are more likely to

reflect sound business decision than earnings manipulation. We emphasize, however, that

although, earnings management measures that associate positively with future performance are

unlikely to reflect strategic earnings manipulation, they may reflect income smoothing (Guay et

al. 1996; Subramanyan 1996).  

In addition, researchers’ choice of earnings manipulation metrics will continue to be

important as the business environment creates changes in firms’ operations that are likely to impact

measures of earnings manipulation. For example, the decline in the return on R&D investment

documented by Curtis et al. 2015 will result in cuts in R&D stemming from prudent business

decisions, not earnings management. If the models used to estimate normal earnings and real

business activities do not adjust for these changes, we will continue to misclassify sound business

decisions as manipulation.

 

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2. BACKGROUND AND HYPOTHESIS DEVELOPMENT

2.1 Hypothesis 1

Schipper (1989) and Healy and Wahlen (1999) define earnings manipulation as managers’ use of

judgments in financial reporting and in structuring transactions to either mislead some stakeholders

about the underlying economic performance of the firm, or to influence contractual outcomes.

Earnings manipulation to mislead stakeholders or influence contractual outcomes may be achieved

through accruals, real-activities, and classification shifting.9

Earnings manipulation should result in declining future operating performance when

accruals reverse or the implications of real earnings management are realized (e.g., Ahmed et al.

1999; Fan 2007; Bowen et al. 2008; Dechow et al. 2010; Dechow et al. 2012; Huffman and Lewis-

Western 2015). In contrast, prior research indicates that the relation between earnings

manipulation and future returns differs depending on the motivation underlying the earnings

manipulation (e.g., signaling versus misleading) and market efficiency (e.g., Subramanyam 1996;

Guay et al. 1996; Fan 2007). Thus, we exclude returns from our main analysis, but we examine

the association between the measures of earnings manipulation and future returns in robustness

analyses.

The evidence with respect to earnings manipulation and analysts’ forecast errors is mixed.

Bradshaw et al. (2001) and Elliot and Philbrick (1990) conclude that analysts do not adjust their

forecasts for earnings management (via abnormal reporting or changes in accounting principles),

suggesting that earnings manipulation will be associated with larger analysts’ forecast errors. Other

                                                            9 We note that earnings management undertaken to influence contractual outcomes may not influence analysts’ forecast errors to the same extent as market-motivated earnings manipulation (i.e., earnings management motivated to mislead some stakeholders) (Dechow et al. 2010; Huffman and Lewis-Western 2015). To the extent that market-based incentives motivate a reasonable percentage of managers’ earnings management actions (Dichev et al. 2013), then we should find an association between measures of earnings manipulation and analysts’ forecast errors.

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research provides the opposite conclusion (e.g., Burgstahler and Eames 2003; Coles et al. 2006).

If some material portion of earnings manipulation influences analysts, then we should observe an

increase in analysts’ forecast errors when managers employ more earnings manipulation in their

financial reports. At a minimum, we should not find an association between earnings manipulation

and analysts’ forecast errors if analysts are fully able to account for earnings manipulation in their

analyses. Finding a negative association between earnings management proxies and analysts’

forecast errors, however, would indicate that the earnings management proxies do not reflect

earnings manipulation.

Based on this discussion, our main analyses focus on future operating performance and

analysts’ forecast errors. We expect that earnings management measures that are positively

associated with future operating performance and are negatively associated with analysts’ forecast

errors are unlikely to reflect earnings manipulation.10 This logic leads to our first hypothesis (in

alternative form):

H1: Accrual-, real-, and special-items-based earnings manipulation measures are negatively associated with firms’ future operating performance, and positively associated with analysts’ forecast errors.

We test H1 by examining the relation between earnings manipulation measures and future

operating performance and future analysts’ forecast errors, while controlling for (1) time-varying

factors that have been found to be associated with earnings manipulation measures (like innate

earnings quality, growth, and performance) and (2) time-invariant, persistent, firm characteristics

(by including firm fixed effects).

                                                            10 Earnings management that positively correlates with future performance may occur from earnings smoothing, a type of discretionary reporting not considered in this study (Subramanyam 1996; Dechow and Skinner 2000; Tucker and Zarowin 2006; Badertscher, Collins, and Lys 2012; Demerjian et al. 2015) or because of model misspecification (e.g., Bowen et al. 2008; Dechow et al. 2010).

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2.2 Restatements

We also explore a setting where we expect managers to engage in earnings manipulation:

prior to earnings restatements. If the earnings management measures we consider capture strategic

earnings manipulation and restatements obtain when managers engage in earnings manipulation,

then we expect to observe a positive association between the earnings management measures and

restatements.

3. DATA, VARIABLE DEFINITIONS AND DESCRIPTIVE STATISTICS

The sample period begins in 1990 since we require cash flow statement information to measure

accruals. We calculate earnings manipulation, future performance, and control variables using data

from the Center on Research in Security Prices (CRSP) and the unrestated Compustat quarterly

file, which provides firms’ financial statement data as originally reported. We obtain restatement

data from Hennes et al. (2008) and the Audit Analytics database, and analysts’ forecast data from

I/B/E/S. We identify SEOs, which we use for a robustness analysis, from the Security Data

Company New Issues database.

We restrict the sample to firms that have sufficient data to calculate the earnings

manipulation, future performance, and control variables. We do not require firms to be covered by

analysts. We do so to retain a more comprehensive sample. As a result, analyses examining

analysts’ forecast errors utilize a smaller subset of our complete sample. We also exclude

observations with accounting changes, merger or acquisition activity, or discontinued operations,

following McNichols (2002), and we exclude regulated industries (e.g., financial institutions and

utilities) since earnings manipulation models are not well suited for these firms (e.g., Dechow et

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al. 2011).11 These requirements yield a final sample of 188,309 quarterly observations from 1990-

2011.12

3.1 Variable Definitions

3.1.1 Measures of Earnings Manipulation

We examine three types of earnings manipulation: (1) accruals, (2) real operating activities

management, and (3) classification shifting. We measure accruals-based earnings manipulation as

the residual from a cross-sectional Jones-type model (AbnAcc) (e.g., DeFond and Subramanyam

1998) and as discretionary revenue (AbnRev), following Stubben (2010). The empirical proxies

we use to measure real earnings manipulation are abnormal decreases in discretionary expenses

(AbnExp), excess production (AbnProd), and abnormally low cash flows (AbnCFO)

(Roychowdhury 2006; Cohen and Zarowin 2010; Gunny, 2010).13 Finally, our proxy for

classification shifting is the strategic classification of recurring expenses as one-time special items,

which we label “low-quality special items” (LQSI), following Cain et al. (2013). Appendix B

provides measurement and estimation details. Moreover, we provide a detailed list of the variables

used in our analyses and their definitions in Appendix C.

In the empirical analysis (and similar to Demerjian et al. 2015), we standardize the accrual,

real, and special items earnings manipulation variables by transforming them to have a standard

                                                            11 Specifically, we exclude firm quarters where ACCHGQ_FN, AQAQ_FN or DOQ_FN are not blank. We identify regulated industries as SIC codes between 4000 and 4900 and between 6000 and 6399. We exclude discontinued operations per McNichols (2002), but acknowledge that discontinued operations could be used to manage earnings as evidenced in Barua, Lin, and Sbaraglia (2010). Barua et al. (2010), however, find that earnings management via discontinued operations has declined since the passage of SFAS 144 (which was effective for fiscal years beginning after 12/15/2001) suggesting that this form of earnings management is not prevalent in recent time periods. 12 We focus on quarterly financial reports because many types of earnings management incentives vary by quarters (e.g., debt covenants thresholds must be met quarterly and market expectations vary by quarter). We find similar results with annual data. 13 Quarterly measures of discretionary accruals and discretionary real-activities-based earnings management have been used extensively in the literature (e.g., Coles et al. 2006; Stubben 2006; Baker et al. 2009; Das et al. 2009; Cohen et al. 2010; Call et al. 2014; Franz et al. 2014).

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deviation of one (their mean is already zero since they are errors from regression models), and then

we decile rank the standardized variable.

3.1.2 Future Consequences

We hypothesize that the primary consequences of earnings manipulation are deterioration

in future operating performance (when accruals reverse or the impact of sub-optimal operating

decisions are realized), and analysts’ increased difficulty in assessing and predicting core operating

earnings. Therefore, we measure the consequences of earnings manipulation with three proxies.

First, future return on assets (ROA) reflects the deterioration of future performance as a result of

earnings manipulation. We define ROA as industry-adjusted operating income after depreciation,

scaled by average total assets.14 Ex-ante, it is unclear at what point in time accruals will reverse or

the impact of sub-optimal reporting decisions will occur. Therefore, we measure FutureROA as

the decile rank of the average of ROA over quarters q+1 to q+4.15 The second future performance

variable we consider is future operating cash flows (CFO). We expect measures of earnings

manipulation either to not correlate with future cash flows (e.g., accruals) or to correlate negatively

with future cash flows (e.g., real earnings management). We define CFO as industry-adjusted

operating cash flows scaled by average total assets. Similar to the way we define FutureROA, we

measure FutureCFO as the decile rank of the average of CFO over quarters q+1 to q+4.

Our third future consequence variable is analysts’ earnings forecast errors (ForecastError).

This variable captures the increased difficulty in assessing and predicting the firm’s core operating

earnings in the presence of earnings manipulation. We measure ForecastError as the absolute

                                                            14 We industry-adjust FutureROA (FutureCFO) by subtracting from the firm’s ROA (CFO) the median ROA (CFO) of all Fama and French industry, year, and quarter counterparts because “normal” operating performance varies across industries and over time (Holthausen and Larcker 1996; Gunny 2010). As discussed in section 4.3, our results are not sensitive to this decision as we find similar results when we do not industry adjust ROA/CFO and, instead, include industry fixed effects in the model. 15 As discussed in section 4.3, in robustness tests we examine future performance over the 24 months following quarter q and we find similar results.

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value of actual earnings in quarter q+1 less the most recent median analysts’ earnings forecast for

quarter q+1.

3.1.3 Restatements

Restate is an indicator variable coded one in years where the firm restates earnings. We

identify restatements from Hennes et al. (2008) for 1997-2006 and from the Audit Analytics

database for 2007 onward.

3.1.4 Control Variables

Following Dechow and Dichev (2002), Hribar and Nichols (2007), and Demerjian et al.

(2013), we include control variables for innate earnings quality, growth, and abnormal returns in

all of our estimations. We include the following earnings quality variables: firm size, sales

volatility, cash flow volatility, operating cycle, percentage of losses, and the number of analysts

following the firm. Following Becker et al. (1998) we also control for whether or not the

company’s audit firm is a Big N audit firm. Following Demerjian et al. (2013), we include controls

for sales growth, the firm’s market-to-book ratio, and current quarter abnormal returns. We provide

details on the calculation of each variable in Panel B of Table 1. Finally, we include firm fixed

effects in all models in order to control for all other persistent firm characteristics that are difficult

to observe.

3.2 Descriptive Statistics

Table 1 provides descriptive statistics. Sample firms, on average, have market

capitalization of 1,349 million and sales volatility of 22 percent. About 11 percent of firm quarters

are characterized by a restatement (Restate) and mean and median earnings manipulation is near

zero, as expected, as they are residuals from regression models (AbnAcc, AbnRev, LQSI, AbnExp,

AbnCFO, AbnProd).

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We examine univariate Pearson correlation coefficients in Table 2. Future operating

performance (FutureROA) is negatively correlated with analysts’ forecast errors (ForecastError)

and with restatements (Restate). Interestingly, Table 2 suggests that “abnormal accruals” measured

from the Jones-type model (AbnAcc) and abnormal expenses (AbnExp) are positively correlated

with future performance. Further, AbnAcc and AbnExp are negatively associated with analysts’

forecast errors and with restatements (Restate), although the correlation between AbnExp and

Restate is not significantly different from zero. In contrast, discretionary revenue (AbnRev), our

second measure of accrual-based earnings management, low-quality special items (LQSI),

abnormal cash flows (AbnCFO), and overproduction (AbnProd) are negatively or not significantly

correlated with future performance, and positively correlated with analysts’ forecast errors and

restatements (Restate).

Thus, in the univariate analysis, AbnRev, LQSI, AbnCFO, and AbnProd exhibit

characteristics we expect of earnings manipulation tools (i.e., they are negatively associated with

future firm performance and/or positively associated with analysts’ forecast errors and

restatements), while AbnAcc and AbnExp do not.

4. TEST DESIGN AND RESULTS

4.1 Consequences of Earnings Manipulation (Hypotheses 1)

To test H1 (i.e., to investigate whether accrual-, real-, and special items-based earnings

manipulation measures are negatively correlated with future firm performance), we estimate the

following model:

Consequencesq+t  = α + α1EarningsManagementq + α2Sizeq + α3SalesVolatilityt-4,t + α4CashFlowVolatilityt-4,t + α5OperCyclet-4,t + α6Lossest-4,t + α7NationalAuditorq + α8SalesGrowthq + α9MBq + α10AbnormalReturnq

+∑ + εq+t (1)

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We measure EarningsManagement with one of the six earnings management measures

discussed in Section 3.1.1 (AbnRev, AbnAcc, DisExp, DisCFO, DisProd, LQSI) We measure

consequences with future earnings (FutureROA), future operating cash flows (FutureCFO), and

future analysts’ forecast errors (ForecastError). Because measuring earnings manipulation is

challenging and the ability of earnings manipulation models to capture discretionary reporting

varies across firms (Dechow et al. 1995; McNichols 2000; Kothari et al. 2005), we include firm

fixed effects in all models. This design allows us to use the firm as its own control. For example,

when future performance is the dependent variable, the firm fixed effect absorbs the firm’s average

level of performance and then allows us to examine whether increases in measures of earnings

manipulation, are associated with decreases in future ROA, beyond the firm’s average level.

Because we include firm fixed effects in all models, our results provide evidence on changes in

earnings manipulation consequences within firms and across quarters. For equation (1) and all

subsequent equations, we report robust standard errors, clustered by firm.

We report the results from the estimation of future earnings performance (FutureROA) in

Table 3. Columns 1 and 2 include the first accrual-based earnings manipulation proxy, abnormal

accruals calculated from the Jones-type model (AbnAcc), while columns 3 and 4 include the second

accrual-based earnings manipulation proxy, discretionary revenue (AbnRev). Prior research

suggests that combing all three measures of real-activities based earnings manipulation (i.e.,

AbnExp, AbnCFO, AbnProd) may result in double-counting or off-setting (Roychowdhury

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2006).16 Thus, following prior research, we do not include AbnExp and AbnCFO in the same

model.17

We expect the earnings management measures to be negatively associated with FutureROA

if they capture strategic earnings manipulation. Table 3 provides evidence that AbnAcc is

positively associated with future performance in both columns 1 and 2. In contrast, AbnRev, the

second accrual-based earnings manipulation measure, is negatively associated with future

performance in both columns 3 and 4. As all models include firm fixed effects, these results suggest

that higher-than-average AbnAcc are associated with significantly higher future earnings

performance, and thus, AbnAcc may not reflect earnings manipulation since earnings manipulation

should result in declines in future operating performance (e.g., Bowen et al. 2008).18 In contrast,

when firms report greater discretionary revenue, future earnings performance over the next four

quarters is significantly lower, consistent with AbnRev reflecting earnings manipulation. In

addition, LQSI is significantly negatively associated with future earnings performance also

suggesting that classification shifting reflects earnings manipulation.

Further, Table 3 suggests that abnormal cuts in expenses (AbnExp) are significantly

positively associated with future earnings performance (coefficient = 0.064, p < 0.01).19 This result

indicates that abnormal cuts in R&D and advertising are associated with improved future

                                                            16 For example, overproduction combined with lenient credit terms results in higher than expected production and lower than expected cash flows. In contrast, cutting discretionary expenses results in higher than expected cash flows (Roychowdhury 2006). We have reverse coded AbnCFO so that it is increasing when cash flow is lower than expected. Thus, we do not include all three metrics in one equation, or in a combined variable. 17 Our results are not sensitive to this decision. We find similar results as those tabulated if we include all real earnings management metrics in the same model. 18 This result is consistent with Subramanaym (1996) who finds a positive association between abnormal accruals from the cross-sectional version of the Jones (1991) model and future operating performance. 19 In our calculation of discretionary expenses (i.e., R&D and SG&A), we set missing values equal to zero. To ensure that this research design choice is not driving the reported results, we re-estimate the model separately for firms with non-missing values of discretionary expenses and continue to find that AbnExp is significantly and positively associated with future performance.

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performance. Recent research provides an explanation for this result. Curtis et al. (2015) find that

the return on investment associated with R&D has declined by about 70 percent in recent years

(i.e., beginning in about the mid-1990s relative to the 1980s). A decline in the return on investment

would indicate that many firms’ R&D projects have marginal benefits that no longer exceeds the

costs, and thus cuts in these investments will result in improved future performance. Therefore,

abnormal cuts in expenses, on average, likely reflect prudent decision making instead of strategic

earnings manipulation particularly for the latter part of our sample.

In contrast, both AbnCFO and AbnProd are negatively associated with FutureROA. Thus,

over producing and/or extending lenient credit terms are examples of costly real-activities based

earnings manipulation. In summary, the results reported in Table 3 provide evidence that two

commonly used measures of earnings manipulation, AbnAcc and AbnExp, do not exhibit a negative

association with firm’s future earnings performance.

Table 4 presents the results from the estimation of future operating cash flows

(FutureCFO). In testing H1, we expect the earnings management measures to not be positively

associated with FutureCFO if they capture earnings manipulation. Similar to the results in Table

3, both AbnAcc and AbnExp are significantly positively associated with FutureCFO. Further, LQSI

is also significantly positively associated with FutureCFO when AbnAcc are included in the

estimation (see columns 1 and 2) but insignificantly associated with FutureCFO when AbnRev is

included in the estimation (see columns 3 and 4). All other measures, AbnRev, AbnProd, and

AbnCFO are significantly negatively associated with FutureCFO suggesting that these measures

capture earnings manipulation.

Prior research argues that accrual-based earnings manipulation does not impact cash flows

(Cohen et al. 2008; Fan 2007; Badertscher 2011); thus, the negative association between AbnRev

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and FutureCFO is surprising. To explore if this relation is attributed to managers engaging in

accrual earnings management to offset unexpected declines in performance (i.e., to further ensure

that performance is not a correlated omitted variable), we supplement the model with the change

in cash flow over the quarter and an indicator for firms whose sales growth declined in quarter q

relative to quarter q-1. We find that the change in cash flow is associated with higher future cash

flows and that declines in sales growth are associated with lower future sales, but the negative

association between AbnRev and FutureCFO remains. Thus, either accrual based earnings

management does have real costs or firms utilize a combined strategy of accrual and real earnings

management and some of the impact of real earning management is being attributed to AbnRev.

We next investigate analyst forecast errors.

Table 5 presents the results investigating the relation between the earnings management

metrics and one-quarter forward analysts’ forecast errors, defined as forecasts EPS less actual EPS.

Thus, positive errors indicate optimistic forecasts. We find that quarters characterized by greater-

than average accruals (AbnAcc) or decreases in discretionary expenses (AbnExp) are not associated

with future analysts’ forecast errors. In contrast, quarters associated with greater amounts of

AbnRev, AbnProd, AbnCFO, or LQSI are associated with larger future analysts’ forecast errors.

Thus, consistent with the results in Tables 3 and 4, the evidence in Table 5 suggests that AbnAcc

and AbnExp do not reflect strategic earnings manipulation.20

The results thus far provide an important insight: abnormal accruals calculated from the

Jones-type model (AbnAcc) and abnormally low discretionary expenses (AbnExp) do not exhibit

the negative association with future performance and the positive association with analyst forecast

errors that one would expect if the variables, on average, reflected earnings manipulation.

                                                            20 We acknowledge that the analyst forecast test is not conclusive as earnings manipulation could not associate with forecast errors if analysts are able to identify and adjust for the earnings manipulation.

18  

4.2 Restatements

Our analyses thus far provide evidence suggesting that two common measures of earnings

manipulation (abnormal accruals from Jones-type models and discretionary cuts in expenses) do

not exhibit characteristics we expect of strategic earnings manipulation. Next, we explore whether

the earnings manipulation measures we consider are associated with restatements.

The results for earnings restatements are presented in Table 6. We estimate the restatement

model using a conditional logistic regression because Allison (2005, 2009) provides evidence that

the conditional logistic model estimated with firm fixed effects is least susceptible to the omitted

variable bias and produces consistent estimates. Note that the sample size is reduced because firm

fixed effects models with binary dependent variables require both outcomes of the dependent

variable for estimation of coefficients. Thus, firms that have never had a restatement are excluded

from the estimation. Consistent with the results in Tables 3-5, only AbnRev, LQSI, AbnProd, and

AbnCFO are significantly positively associated with earnings restatements, suggesting that these

measures indeed capture strategic earnings manipulation.21

On the other hand, the results in Table 6 suggest that AbnAcc are significantly negatively

associated with earnings restatements and AbnExp are not significantly associated with earnings

restatements. In untabulated analyses, we find parallel results to those reported when we exclude

the firm fixed effects and cluster standard errors by firm and year. This evidence continues to

suggest that Jones-type abnormal accruals and abnormally low discretionary expenses do not

exhibit the characteristics we expect of strategic earnings manipulation.

                                                            21 The association between real earnings management and restatements may seem counterintuitive, but Badertscher (2011) provides evidence that real earnings management is used after accrual earnings management and thus corresponds with more aggressive earnings management strategies that cumulate with materially misstated or fraudulent reporting (i.e., leading to restatements).

19  

4.3 Robustness Analyses

In order to ensure that our results are robust to potential alternative explanations, we

perform several untabulated analyses. First, we re-estimate Tables 3-5 excluding firm fixed-

effects and our inferences remain unchanged. Second, Ecker et al. (2013) provide evidence that

estimating the accrual models for firms of similar size rather than by industry reduces sample

attrition and results in more powerful tests. We estimate all of the accrual and real operating

activities models by size groups where the groups are defined annually and we find similar results

to those reported.22 Specifically, we continue to find that AbnAcc and AbnExp are significantly

positively associated with future performance and negatively associated with analysts’ forecast

errors while the other measures (AbnRev, AbnProd, and AbnCFO) are negatively associated with

future performance and positively associated with forecast errors.

Third, Gomley and Matsa (2013) find that including industry fixed-effects instead of

industry adjusting the dependent variable provides a cleaner measure of industry-adjusted

performance. In order to ensure our results are not sensitive to this choice, we re-estimate the

future ROA and CFO regressions including industry fixed effects and the unadjusted performance

metrics. Our results remain unchanged. Specifically, abnormal revenue, cash flows, production,

and low quality special items are significantly negatively associated with future ROA and future

CFO, while discretionary expenses and abnormal accruals are positively associated with future

performance. We also explore the sensitivity of our results to examining future performance over

the next 24 months rather than 12 months and we find similar results to those reported previously.

Fourth, prior research provides evidence that firms engage in earnings management prior

to an SEO (Cohen and Zarowin 2010). Thus, we examine if inferences from the SEO setting

                                                            22 We chose 60 size groups because each year we have about 3,000 firms and Ecker et al. (2013) included 50 firms in each group. Thus, 3,000/50 = 60.

20  

change depending upon the measures of earnings management employed. We create two summary

earnings management measures, one calculated using only the proxies that capture earnings

manipulation, and one calculated using proxies that are common in the literature (including those

that do not appear to reflect earnings manipulation). The first, TotalEM, is calculated using

AbnRev, AbnProd, AbnCFO, and LQSI. The second, TotalEM2, includes AbnAcc, AbnExp,

AbnProd, and LQSI.23 We estimate equation 1 on a subset of quarters associated with SEOs. We

first estimate the model on the full SEO sample (columns 1 and 2), and then pre- (columns 3 and

4) and post- (columns 5 and 6) Sarbanes-Oxley Act (SOX). Cohen et al. (2008) find that after

SOX, firms shifted away from accruals-based earnings management towards real earnings

management. Thus, we investigate whether our inferences change in the pre- and post-SOX

periods. The results in Table 7 suggest that both TotalEM and TotalEM2 are significantly

negatively associated with FutureROA during SEO quarters for the full sample (see columns 1 and

2) and for the pre-SOX (columns 3 and 4) period. Thus, it appears that SEOs create strong

incentives for firms to manage earnings no matter how we define earnings manipulation. The

results in the post-SOX period (columns 5 and 6), however, suggest that post-SOX, TotalEM is

significantly negatively associated with FutureROA while TotalEM2 is not associated with

FutureROA. Thus, to the extent that earnings management prior to an SEO should correlate with

poor subsequent performance, these results suggest that in the more recent period, TotalEM

captures strategic earnings manipulation while TotalEM2 does not.24

                                                            23 We exclude DisCFO because, as mentioned previously, prior work argues that combining DisExp and DisCFO into one metric can results in double counting or offsetting. 24 Note that this result is not inconsistent with recent work examining earnings management near SEOs (Kothari et al. 2015). Kothari et al. (2015) measure earnings management with abnormal cuts to discretionary expenses, but the sample used in Kothari et al. (2015) spans 1970-2012 and thus most of the SEOs in their sample occur prior to SOX.

21  

Next, we investigate the association between the measures of strategic earnings

manipulation and future abnormal returns. Specifically, we market adjust firm returns one quarter

of fiscal quarter q and re-estimate equation (1). We present the results in Table 8. Consistent with

our future performance results, AbnRev, LQSI, AbnProd and AbnCFO are all significantly

negatively associated with future firm returns one quarter ahead (see Table 8). In contrast, AbnAcc

and AbnExp are significantly positively associated with future firm returns one quarter ahead (see

Table 8). Overall, the return results suggest that the market negatively prices strategic earnings

manipulation measures as manipulation is revealed in subsequent performance, but it positively

prices Jones-type abnormal accruals and abnormally low discretionary expenses.

Finally, in untabulated analyses we explore which earnings manipulation measures are

more costly, in terms of future performance. To do this, we combined the two real earnings

manipulation metrics (AbnProd and AbnCFO) following Cohen et al. (2008) and Cohen and

Zarowin (2010) to form one real-activities-based metric and we compare the costs of accrual, real,

and classification shifting earnings manipulation using F tests of differences in coefficient

magnitudes. The results from this analysis suggest that real earnings manipulation from

overproduction (AbnProd) and extending lenient credit terms (AbnCFO) have a significantly

stronger negative effect on future operating performance than accrual-based earnings manipulation

(AbnRev). Moreover, classification shifting (LQSI) has a stronger positive association with

analysts’ forecast errors than either real or accrual-based earnings manipulation (AbnRev). Overall,

these results suggest that low-quality special items and real-based earnings manipulation activities

are, on average, more costly than accrual-based earnings manipulation activities.

 

22  

5. CONCLUSION

Earnings manipulation measures should be negatively correlated with future performance

when accruals reverse or the implications of real earnings manipulation are realized, but a

comprehensive analysis of these relations is not provided in the extant literature. We investigate

accrual-, real- and special items- based earnings manipulation metrics and their effect on firms’

future operating performance, analysts’ forecast errors, and restatements. We find that two

commonly used measures of earnings manipulation (i.e., cross-sectional Jones-type abnormal

accruals and abnormal declines in discretionary expenses) are associated with increases in future

performance, decreases in restatement rates, and not associated with analysts’ forecast errors.

Thus, these measures lack characteristics we would expect in actual earnings manipulation. The

results, however, are consistent with prior work documenting a positive association between Jones-

type abnormal accruals and future performance (Subramanyam 1996; Guay et al. 1996) and

suggest that Jones-type abnormal accruals and abnormal cuts in discretionary expenses may reflect

strategic business decisions, smoothing, or signaling. Nevertheless, our results are important as

current research continues to use Jones-type abnormal accruals and abnormal cuts in discretionary

expenses as proxies for strategic earnings manipulation.

We find that the remaining measures (discretionary revenue, low-quality special items,

excess production, and abnormally low cash flow from operations) are associated with declines in

future performance, increases in analysts’ forecast errors, and an increased propensity for

restatements relative to the firm’s own averages in quarters characterized by less earnings

manipulation. Thus, these measures possess characteristics expected of strategic earnings

manipulation.

23  

This study makes several contributions to the earnings management literature. First, our

analyses provide evidence on the extent to which commonly used earnings management proxies

exhibit characteristics we would expect of strategic earnings manipulation and complement

research that assesses the validity of the earnings management measures using alternative methods.

An important implication of our study is that some evidence from prior research documenting an

increase in abnormal accruals (using Jones-type models) and abnormal cuts in discretionary

expenses, may not actually indicate strategic earnings manipulation, but instead prudent business

decisions.

Further, we note that researchers’ choice of earnings manipulation metrics will continue to

be important as the business environment creates changes in firms’ operations that are likely to

impact measures of earnings manipulation. For example, the return on R&D investment has

declined in recent years; thus cuts in R&D are now more likely to reflect prudent business

decisions, not earnings management. If the models of normal earnings and real business activities

do not adjust for these changes, we will continue to misclassify sound business decisions as

manipulation.

 

24  

Appendix A

This appendix tabulates the number of studies in the top five accounting journals, over the period 2012-2014, that use the earnings management metrics we consider. Earnings management studies were identified by searching on the keyword “earnings management.” Many of the studies use more than one earnings management metric, which is why the journal columns may not sum to the row totals. The last row tabulates the total number of earnings management studies (i.e., those that do and do not use the earnings management metrics we consider) published over the period of consideration.

The Accounting

Review

Journal of Accounting Research

Journal of Accounting

and Economics

Contemporary Accounting Research

Review of Accounting

Studies

AbnAcc 6 2 5 6 5 AbnRev 1 0 0 0 2 AbnExp 2 0 1 1 1 AbnCFO 2 0 1 1 1 AbnProd 2 0 1 0 1 Class. Shifting

3 1 2 1 0

TOTAL: Studies using at least one of the metrics

9 (60%) 3 (75%) 7 (28%) 6 (55%) 6 (37.5%)

TOTAL: Earnings management studies

15 4 25 11 16

25  

Appendix B Measurement of Earnings Manipulation

We examine three types of earnings manipulation: 1) aggressive accruals, 2) real operating

activities management (e.g., Cohen and Zarowin, 2010; Gunny, 2010; Zang, 2012), and strategic

classification of recurring expenses as one-time special items (Cain et al., 2013). We begin with

the first of two accrual-based earnings manipulation measure, discretionary accruals estimated

using the cross-sectional version of the Jones (1991) model.25 Specifically, we estimate the

following model by Fama and French (1997) industry, year and quarter:

(A1),

where total accruals (TA) are measured as the difference between cash from operations and earnings in

quarter q. AT is average total assets, ∆Sales is the change in sales over quarter q-1 to quarter q, and

PPE is property, plant and equipment in quarter q. The residual from this model serves as our first

measure of accruals earnings manipulation (AbnAcc).

Our next measure of accruals-based earnings manipulation is discretionary revenue

(AbnRev). 26 Stubben (2010) finds that discretionary revenue is a powerful method of detecting

earnings manipulation. Per Stubben (2006), we estimate the following model by Fama and French

(1997) industry, year and quarter:

(A2).

                                                            25 We examine the cross-sectional version of the Jones (1991) model rather than the modified Jones model (Dechow et al. 1995). We do so because we investigate the average costs of earnings management, and therefore we are not examining a specific treatment period making implementation of the modified Jones model challenging, as it requires adjusting for credit sales during the treatment period (Dechow et al. 1995). However, we use the firm-fixed effects design in order to control for the firm’s normal level of each earnings management metric so that any deviation from the normal amount is considered “abnormal.” Similarly, we do not use performance-matching to identify abnormal reporting as the firm-fixed effect effectively uses the firm’s average level of reporting discretion as the benchmark and any deviations from the firm’s average are considered abnormal. 26 The quarterly measure of discretionary revenue has been used in prior research (e.g., Call et al. 2014).

26  

We have defined all variables previously. Our measure of discretionary revenue, AbnRev, is the

error term ( ).

Following Cohen and Zarowin (2010) and Roychowdhury (2006) we use three measures

of real earnings manipulation activities: cutting discretionary expenses (to increase income),

extending credit to more risky customers (to increase sales) and overproducing inventory (to

reduce per unit costs). We estimate the models by Fama and French (1997) industry, year and

quarter. We measure expected discretionary expenses (given the level of sales) with the following

model:

∆ (A3).

Exp is the sum of research and development and selling, general and administrative

(SG&A) expenses in quarter q. The residual in model A3 reflects abnormal discretionary expenses,

where abnormally low discretionary expenses are consistent with income-increasing real earnings

manipulation. Therefore, we multiply the error term by negative one so that it is increasing in the

extent of real earnings manipulation (AbnExp).

We measure expected cash flows (given the level of sales) with the following model:

∆ (A4).

CFO is measured as operating cash flows less extraordinary items and discontinued

operations reported in the Statement of Cash Flows. The residual in model A4 reflects abnormal

cash flows, where lower than expected cash flows are the result of selling more product to

customers on credit than in cash. Thus, we multiply the error term by negative one so that it is

increasing in the extent of real earnings manipulation (AbnCFO).

27  

We measure production (PROD) as costs of goods sold plus the change in inventory. We

estimate normal production with the following model by Fama and French (1997) industry, year

and quarter:

0 01

1 2∆

3 (A5).

The residual from this model serves as our measure of real earnings manipulation from over

production (AbnProd).

Finally, low-quality special items are those that contain recurring expenses and are

estimated from the following equation (Cain et al. 2013):

, = ×UE_NOAi,t-1+ ×UE_CEi,t-1+ ×UE_∆CEi,t+1+ ×UE_∆CEi,t+2 (A6).

Model (6) is replicated from Cain et al. (2013, equation 7). UE_NOA is unexpected net

operating assets; UE_CE is unexpected core earnings; and UE_∆CE is the unexpected change in

core earnings. All variables are scaled by total sales. We then multiply LQSI by sales and divide

by average total assets so that the variable is on a similar scale as the other earnings management

metrics. Finally, since Cain et al. (2013) assess the quality of special items on an annual basis (i.e.,

estimate equation A6 using annual data), we calculate the variable on an annual basis and assume

that years with low-quality special items result in four quarters of low quality special items.

In the empirical analysis (and similar to Demerjian et al., 2015), we standardize the accrual,

real, and special items earnings management variables by transforming them to have a standard

deviation of one (they are already mean zero as they are errors from regression models), and then

we decile rank the standardized variables.

28  

Appendix C

Variable Description Definition

Earnings Management To facilitate comparison across metrics and to ensure robust conclusions from analyses using the combined earnings management metrics, we standardize all metrics to have a

standard deviation of one (their mean is already zero as they are errors from a regression). We then decile rank the metrics. Thus, the standardized and ranked variables are used in

analyses. We estimate all expectations models by Fama and French (1997) industry, year, and quarter.

AbnAcc Abnormal Accruals

Accrual-based earnings management: Abnormal accruals are the residual from the cross-sectional version of the Jones model (Jones 1991)

AbnRev Abnormal Revenue

Accrual-based earnings management: Abnormal revenue is the residual from the accounts receivable model of Stubben (2006, 2010).

LQSI Low Quality Special Items

Income shifting earnings management: Recurring expenses misclassified as special items and are estimated following Cain et al. (2013).

AbnExp Abnormal Cuts in Expenses

Real-activities-based earnings management: The residual from the model of normal discretionary expenses (Roychowdhury 2006). We multiply the residual by negative one so that it is increasing in the real earnings management activity.

AbnCFO Abnormal CFO

Real-activities-based earnings management The residual from the model of normal cash flow from operations (Roychowdhury 2006). We multiply the residual by negative one so that it is increasing in the real earnings management activity.

AbnProd Abnormal Production

Real-activities-based earnings management: The residual from the model of normal production (Roychowdhury 2006).

RealEM Total Real Earnings Manipulation

The sum of AbnCFO and AbnProd.

TotalEM Total Earnings Manipulation

The sum of AbnRev, RealEM, and LQSI.

29  

TotalEM2 Total Earnings Manipulation 2

The sum of AbnAcc, DisCFO , DisExp, and LQSI.

Control Variables

Size Firm Size The natural log of the firm’s assets (AT) reported at the end of year t. Assets in the untransformed variable (i.e., the firm’s total assets).

Market Capitalization

Market Value The firm’s market capitalization at the end of year t defined as shares outstanding (CSHO) multiplied by the price at the end of the fiscal year (PRCC_F).

Sales Volatility

Sales Volatility The standard deviation of [sales (SALE) / average assets (AT)] over at least three of the last five years (t-4, t).

CashFlow Volatility

Cash Flow Volatility

The standard deviation of [cash from operations (OANCF) / average assets (AT)] over at least three of the last five years (t-4, t).

OperCycle Operating Cycle

The natural log of the length of the firm’s operating cycle, defined as sales turnover plus days in inventory [(SALE/360)/(average RECT) + (COGS/360)/(average INVT)] and is averaged over at least three of the last five years (t-4, t).

Loss% Loss History The percentage of years reporting losses in net income (IBC) over at least three of the last five years (t-4, t).

National Auditor

National Auditor Indicator

An indicator variable set equal to one for firms audited by national audit firms in year t; zero otherwise.

Sales Growth

One-year Sales Growth

The decile rank of industry adjusted (by industry, year and quarter) sales growth defined as current year’s sales (SALEt) less prior year’s sales (SALEt-1) less the increase in receivables all scalded by prior year’s sales.

LNumAnalysts Analysts Coverage of Firm

The log of 1 plus the number of analysts following the firm in year t, quarter q.

MB Market-to-book ratio

The market-to-book ratio defined as the firm’s market capitalization (PRCCQ×CSHOQ) divided by book value (SEQQ) for quarter q.

Consequence Variables

AbnRet Abnormal Return over quarter q+1

Market-adjusted, buy-and-hold returns over the quarter following quarter q, where market-returns are value-weighted.

FutureCFO Abnormal CFO over year t+1

The decile rank of future, industry-adjusted operating cash flows, scaled by average total assets (CFO). Future CFO is calculated as the average of CFO overquarters q+1 to q+4

FutureROA Abnormal ROA over year t+1

The decile rank of future, industry-adjusted return on operating income after depreciation, scaled by average

30  

total assets (ROA). Future ROA is calculated as theaverage of ROA over quarters q+1 to q+4.

ForecastError Analysts Forecast Errors in quarter q+1

The quarter q+1 EPS forecast error. Forecast errors arecalculated as the most recent median forecast of EPSless actual EPS less. We scale the forecast error bylagged price.

Restate Restatements

An indicator variable set equal to one in financial reporting periods that are subsequently restated. We identify restatement for years 1997- 2006 from the Hennes et al. (2008) restatement dataset and for years2007 forward from the Audit Analytics database. For the Hennes et al. (2008) data, the restatementannouncement date is known, but not the years beingrestated. Thus, for this data only, we assume low-quality financial reporting in the year that a firm announced a restatement and in the prior two years. Our results are not sensitive to this decision as we findsimilar results if we assume that only theannouncement year and prior year or only theannouncement year are low-quality reporting periods. For audit analytics restatements, the restatementsperiod is provided. Note that restatement data is not available for the earlier years in our sample (prior to1997) and thus the sample size is reduced for this test.

31  

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Table 1 Descriptive Statistics Variable N Mean Median StdDev Assets 188,309 1,436.83 141.60 9,275.83 Market Capitalization 188,309 1,349.36 146.55 4,620.07 SalesVolatility 188,309 0.22 0.15 0.22 CashFlowVolatility 188,309 0.09 0.06 0.10 OperCycle 188,309 4.75 4.80 0.79 Loss% 188,309 0.33 0.20 0.35 NationalAuditor 188,309 0.81 1.00 0.39 SalesGrowth 188,309 0.03 0.00 0.28 LNumAnalysts 188,309 0.75 0.00 0.99 AbnormalReturn 188,309 0.01 -0.02 0.29 FutureROA 188,309 -0.01 0.00 0.05 FutureCFO 188,309 0.00 0.00 0.04 ForecastError 81,981 0.07 0.03 0.10 Restate 129,105 0.11 0.00 0.31 AbnAcc 188,309 0.00 0.00 0.06 AbnRev 188,309 0.00 0.00 0.04 LQSI 188,309 0.00 0.00 0.02 AbnExp 188,309 0.00 0.01 0.07 AbnCFO 188,309 0.00 0.00 0.05 AbnProd 188,309 0.00 0.00 0.07

Notes: The sample is restricted to firms that have sufficient data to calculate the earnings manipulation, future performance, and control variables. We exclude observations with accounting changes, merger or acquisition activity, or discontinued operations, and regulated industries (e.g., financial institutions and utilities). These requirements yield a final sample of 188,309 quarterly observations from 1990-2011. Indicates that the untransformed form of the variable is used in this table only. We use the untransformed variable for ease of interpretation. The note below provides variable definitions.

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Table 2 Univariate Pearson Correlations

FutureROA Forecast

Error Restate AbnAcc AbnRev LQSI AbnExp AbnCFO AbnProd

FutureROA -0.159 -0.046* 0.007* -0.003 0.001 0.071* -0.272* -0.181* ForecastError -0.159 0.028** -0.005*** 0.006*** 0.025*** -0.014** 0.049* 0.012** Restate -0.046* 0.028** -0.014* 0.005* 0.008* -0.006 0.025* 0.018* AbnAcc 0.007* -0.005*** -0.014* -0.301* -0.018* 0.089* 0.577* 0.109* AbnRev -0.003 0.006*** 0.005* -0.301* 0.000 -0.003 -0.259* 0.010* LQSI 0.001 0.025*** 0.007* -0.018* 0.000 0.003 -0.013* -0.011* AbnExp 0.071* -0.014** -0.006* 0.089* -0.003 0.003 -0.095* 0.546* AbnCFO -0.272* 0.049* 0.026* 0.577* -0.259* -0.013 -0.094* 0.337* AbnProd -0.181* 0.012** 0.019* 0.109* 0.010* -0.011* 0.546* 0.337*

Notes: The “*” denotes statistical significance at the 10 percent alpha level or better. Variable definitions are provided in the note accompanying Table 1.

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Table 3 Abnormal Reporting Metrics and Future ROA

Dependent Variable = FutureROAt+1 Expectation (1) (2) (3) (4)

AbnAcc - 0.031*** 0.093*** (15.98) (29.86) AbnRev - -0.038*** -0.050*** (-25.51) (-31.29) LQSI - -0.021*** -0.018*** -0.023*** -0.024*** (-4.31) (-3.67) (-4.61) (-4.79) AbnExp - 0.064*** 0.065*** (11.33) (11.51) AbnProd - -0.097*** -0.060*** -0.091*** -0.061*** (-22.58) (-14.88) (-20.98) (-14.75) AbnCFO - -0.110*** -0.055*** (-26.88) (-18.99) LagROA 0.290*** 0.272*** 0.296*** 0.292*** (53.12) (49.89) (53.66) (52.67)

Size -0.003* -0.003 -0.003* -0.003

(-1.74) (-1.53) (-1.75) (-1.46) SalesVolatility 0.046*** 0.045*** 0.046*** 0.048*** (4.96) (5.00) (5.02) (5.26) CashFlowVolatility 0.111*** 0.106*** 0.113*** 0.114*** (4.65) (4.45) (4.75) (4.77) OperCycle -0.029*** -0.029*** -0.028*** -0.029*** (-5.11) (-5.27) (-5.05) (-5.07) Loss% -0.125*** -0.118*** -0.125*** -0.125*** (-16.63) (-15.84) (-16.67) (-16.59) NationalAuditor 0.006 0.007 0.006 0.005 (0.94) (1.04) (0.90) (0.86) SalesGrowth 0.080*** 0.071*** 0.089*** 0.083*** (41.49) (36.95) (43.46) (39.87) LNumAnalysts 0.001 0.000 0.001 0.000 (0.27) (0.11) (0.25) (0.09) MB 0.003*** 0.003*** 0.003*** 0.003*** (9.02) (9.01) (8.97) (8.72) AbnormalReturn 0.062*** 0.059*** 0.061*** 0.060***

(29.27) (28.29) (28.99) (28.59) Firm Fixed Effects Included  Included Included  Included  N 188,309 188,309 188,309 188,309 R2 0.186 0.193 0.186 0.187

Notes: This table reports the results from the regression of future ROA on earnings manipulation metrics, firm fixed effects, and controls. T-statistics are presented below the coefficients. See Table 1, Panel B for variable definitions. ***, **,* denotes a two-tailed p-value of less than 0.01, 0.05, and 0.10, respectively based on robust standard errors that are clustered by firm.

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Table 4 Abnormal Reporting Metrics and Future CFO Dependent Variable = FutureCFOt+1 Expectation (1) (2) (3) (4)

AbnAcc - 0.021*** 0.084*** (9.79) (24.66) AbnRev - -0.014*** -0.027*** (-9.00) (-15.69) LQSI - 0.007 0.011** 0.005 0.004 (1.25) (2.14) (0.95) (0.84) AbnExp - 0.079*** 0.081*** (13.28) (13.54) AbnProd - -0.132*** -0.090*** -0.129*** -0.095*** (-29.61) (-21.31) (-28.93) (-21.82) AbnCFO - -0.109*** -0.057*** (-23.85) (-16.71) LagCFO 0.062*** 0.059*** 0.064*** 0.065*** (19.45) (19.51) (20.33) (21.09) Size 0.003 0.003 0.003* 0.004* (1.60) (1.41) (1.70) (1.93) SalesVolatility 0.019** 0.020** 0.020** 0.023** (2.05) (2.11) (2.11) (2.42) CashFlowVolatility 0.045* 0.038 0.047* 0.047* (1.80) (1.56) (1.89) (1.92) OperCycle -0.021*** -0.021*** -0.021*** -0.021*** (-3.48) (-3.56) (-3.47) (-3.53) Loss% -0.145*** -0.133*** -0.146*** -0.145*** (-17.82) (-16.79) (-18.05) (-18.12) NationalAuditor 0.019*** 0.020*** 0.019*** 0.019*** (2.96) (3.05) (2.95) (2.93) SalesGrowth 0.012*** 0.005*** 0.013*** 0.007*** (6.76) (3.02) (6.74) (4.03) LNumAnalysts 0.006* 0.006* 0.006* 0.005* (1.96) (1.92) (1.92) (1.80) MB 0.002*** 0.002*** 0.002*** 0.002*** (5.73) (5.51) (5.76) (5.39) AbnormalReturn 0.011*** 0.009*** 0.011*** 0.010***

(5.24) (3.99) (5.15) (4.60) Firm Fixed Effects Included  Included Included  Included  N 188,309 188,309 188,309 188,309 R2 0.052 0.058 0.052 0.051

Notes: This table reports the results from the regression of future CFO on earnings manipulation metrics, firm fixed effects, and controls. T-statistics are presented below the coefficients. See Table 1, Panel B for variable definitions. ***, **,* denotes a two-tailed p-value of less than 0.01, 0.05, and 0.10, respectively based on robust standard errors that are clustered by firm.

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Table 5 Abnormal Reporting Metrics and Analysts’ Forecast Error

Dependent Variable = ForecastErrorq+1 Expectation (1) (2) (3) (4) AbnAcc + 0.000 -0.003 (0.05) (-1.09) AbnRev + 0.008*** 0.009*** (4.90) (5.45) LQSI + 0.015*** 0.015*** 0.015*** 0.015*** (4.88) (4.82) (4.83) (4.85) AbnExp + -0.001 -0.001 (-0.34) (-0.30) AbnProd + 0.009*** 0.007** 0.008*** 0.006** (2.94) (2.30) (2.78) (1.97) AbnCFO + 0.006* 0.006*** (1.81) (2.64) Size -0.006*** -0.006*** -0.006*** -0.006*** (-4.34) (-4.28) (-4.35) (-4.32) SalesVolatility 0.005 0.006 0.005 0.006 (0.93) (0.97) (0.93) (0.97) CashFlowVolatility -0.009 -0.008 -0.009 -0.009 (-0.72) (-0.64) (-0.73) (-0.69) OperCycle 0.009*** 0.009*** 0.009*** 0.009*** (2.71) (2.73) (2.71) (2.73) Loss% 0.008* 0.008 0.008* 0.008 (1.72) (1.54) (1.74) (1.59) NationalAuditor 0.005 0.005 0.005 0.005 (1.24) (1.26) (1.22) (1.23) SalesGrowth -0.012*** -0.011*** -0.016*** -0.015*** (-6.02) (-5.85) (-7.62) (-7.11) LNumAnalysts 0.003* 0.002* 0.002* 0.002* (1.78) (1.77) (1.75) (1.75) MB 0.000 0.000 0.000 0.000 (0.20) (0.21) (0.25) (0.26) AbnormalReturn -0.025*** -0.025*** -0.025*** -0.025***

(-13.65) (-13.62) (-13.42) (-13.36) Firm Fixed Effects Included  Included Included  Included  N 81,981 81,981 81,981 81,981 R2 0.007 0.007 0.007 0.006

Notes: This table reports the results from the regression of future analyst forecast errors on earnings manipulation metrics, firm fixed effects, and controls. T-statistics are presented below the coefficients. See Table 1, Panel B for variable definitions. ***, **,* denotes a two-tailed p-value of less than 0.01, 0.05, and 0.10, respectively based on robust standard errors that are clustered by firm. 

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Table 6 Abnormal Reporting Metrics and Restatements Dependent Variable = Restatementt+1 Expectation (1) (2) (3) (4)

AbnAcc + -0.138*** -0.294*** (-3.25) (-4.11) AbnRev + 0.057* 0.081** (1.86) (2.39) LQSI + 0.501*** 0.490*** 0.512*** 0.513*** (4.05) (3.96) (4.14) (4.15) AbnExp + 0.074 0.061 (0.58) (0.49) AbnProd + 0.245*** 0.174* 0.230*** 0.202** (2.73) (1.87) (2.59) (2.15) AbnCFO + 0.307*** 0.119** (3.40) (2.02) Size 0.201*** 0.207*** 0.200*** 0.203*** (4.28) (4.41) (4.26) (4.32) SalesVolatility 0.022 0.034 0.019 0.023 (0.09) (0.14) (0.08) (0.09) CashFlowVolatility -0.442 -0.390 -0.456 -0.438 (-0.68) (-0.60) (-0.71) (-0.68) OperCycle 0.112 0.111 0.113 0.112 (0.90) (0.89) (0.91) (0.90) Loss% 0.490** 0.431** 0.500** 0.479** (2.47) (2.17) (2.52) (2.42) NationalAuditor 0.079 0.084 0.079 0.081 (0.54) (0.57) (0.53) (0.55) SalesGrowth -0.041 -0.020 -0.033 -0.021 (-1.29) (-0.63) (-0.95) (-0.60) LNumAnalysts 0.009 0.007 0.010 0.009 (0.14) (0.10) (0.15) (0.13) MB -0.023*** -0.023*** -0.023*** -0.023*** (-3.26) (-3.27) (-3.27) (-3.28) AbnormalReturn 0.052 0.059 0.054 0.057

(1.19) (1.35) (1.24) (1.31) Firm Fixed Effects Included  Included Included  Included  N 48,227 48,227 48,227 48,227 R2 0.007 0.007 0.007 0.006

Notes: This table reports the results from conditional logistic estimations of restatement years on earnings manipulation metrics, firm fixed effects, and controls. T-statistics are presented below the coefficients. See Table 1, Panel B for variable definitions. ***, **,* denotes a two-tailed p-value of less than 0.01, 0.05, and 0.10, respectively based on robust standard errors.

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Table 7 Example of Alternative Conclusions Drawn Based on Choice of Abnormal Reporting Measures Dependent Variable = FutureROAt+1 (1) (2) (3) (4) (5) (6) Restricted to SEO Qtrs

All Years Restricted to SEO Qtrs

Pre-SOX Restricted to SEO Qtrs

Post-SOX TotalEM -0.082*** -0.039** -0.062*** (-5.19) (-2.38) (-3.13) TotalEM2 -0.024* -0.037** -0.006 (-1.71) (-2.15) (-0.38) Firm Fixed Effects

Included Included Included Included Included Included

Control Variables

Included Included Included Included Included Included

N 5,214 5,214 2,620 2,620 2,594 2,594 R2 0.090 0.078 0.109 0.107 0.020 0.012

Notes: This table reports the results from the regression of future ROA on earnings manipulation summary metrics, firm fixed effects, and controls. T-statistics are presented below the coefficients. See Table 1, Panel B for variable definitions. ***, **,* denotes a two-tailed p-value of less than 0.10, 0.05, and 0.01, respectively based on robust standard errors that are clustered by firm.

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Table 8 Abnormal Reporting Metrics and Future Returns Dependent Variable = AbnRetq+1 Expectation (1) (2) (3) (4)

AbnAcc ? 0.003 0.027*** (1.58) (10.07) AbnRev ? -0.009*** -0.016*** (-4.88) (-7.83) LQSI ? -0.010*** -0.008** -0.010*** -0.010*** (-3.06) (-2.49) (-3.08) (-3.13) AbnExp ? 0.022*** 0.022*** (6.14) (6.16) AbnProd ? -0.033*** -0.019*** -0.032*** -0.019*** (-12.32) (-6.87) (-12.06) (-7.07) AbnCFO ? -0.042*** -0.027*** (-13.84) (-11.95) Size -0.048*** -0.048*** -0.048*** -0.048*** (-39.10) (-39.26) (-39.10) (-39.09) SalesVolatility 0.004 0.004 0.004 0.005 (0.83) (0.77) (0.84) (0.95) CashFlowVolatility 0.004 0.001 0.004 0.004 (0.26) (0.07) (0.28) (0.26) OperCycle -0.014*** -0.014*** -0.014*** -0.014*** (-5.15) (-5.12) (-5.14) (-5.16) Loss% -0.034*** -0.029*** -0.035*** -0.033*** (-9.12) (-7.69) (-9.20) (-8.76) NationalAuditor -0.012*** -0.012*** -0.012*** -0.012*** (-3.84) (-3.79) (-3.84) (-3.86) SalesGrowth 0.031*** 0.029*** 0.034*** 0.032*** (15.61) (14.38) (16.19) (14.84) LNumAnalysts 0.011*** 0.011*** 0.011*** 0.011*** (7.20) (7.15) (7.21) (7.10) MB -0.001*** -0.001*** -0.001*** -0.001*** (-4.19) (-4.26) (-4.21) (-4.33) AbnormalReturn 0.013*** 0.012*** 0.013*** 0.012***

(5.32) (4.83) (5.21) (4.95) Firm Fixed Effects Included  Included Included  Included  N 188,286 188,286 188,286 188,286 R2 0.028 0.029 0.028 0.029

Notes: This table reports the results from the regression of future (one quarter ahead) market-adjusted returns on earnings manipulation metrics, firm fixed effects, and controls. T-statistics are presented below the coefficients. See Table 1, Panel B for variable definitions. ***, **,* denotes a two-tailed p-value of less than 0.01, 0.05, and 0.10, respectively based on robust standard errors that are clustered by firm.